LuxuryAI
Clienteling Platform

AI-Powered Clienteling

LuxuryAI is an AI-powered clienteling platform designed for luxury retail environments. The platform brings customer intelligence, purchase history, AI-generated insights, product recommendations, appointment preparation, and product knowledge into a single workspace for client advisors.

Instead of requiring advisors to manually search across multiple sources of customer and product information, LuxuryAI transforms these fragmented data sources into actionable customer intelligence .

AI Customer 360

The Customer 360 module consolidates customer profiles, purchase history, behavioral information, and AI-generated insights into a unified customer view.

Structured customer and transaction data is managed through PostgreSQL, while flexible AI-generated profiles and insights are stored in MongoDB. The AI layer can then transform historical customer data into meaningful preferences, behavioral patterns, and clienteling insights.

This allows advisors to understand the customer before an interaction rather than relying solely on manually reviewing historical records.

AI Appointment Intelligence

Luxury retail appointments often require advisors to review customer history, previous purchases, preferences, and recent interactions before meeting a high-value client.

LuxuryAI turns this preparation process into an AI-assisted workflow . Customer information, purchase history, AI insights, and contextual data can be combined by the agent layer to generate a concise appointment briefing.

The workflow is designed around a retrieve → analyze → generate process, allowing the advisor to focus on the client rather than manually collecting information.

Personalized AI Recommendations

The recommendation engine uses customer information and product knowledge to identify products that may be relevant to an individual client.

Customer preferences, purchase history, customer tier, and AI-generated insights can be combined with product information to create personalized product matching .

The architecture is designed to support retrieval-augmented generation, allowing recommendations to be grounded in relevant product knowledge rather than relying entirely on the language model's internal knowledge.

RAG Product Knowledge Assistant

Luxury retail advisors need accurate access to product information, including collections, product details, ingredients, fragrance notes, and other brand knowledge.

The Knowledge module is designed around Retrieval-Augmented Generation (RAG) . Instead of relying exclusively on an LLM's internal knowledge, the system retrieves relevant product information before generating an answer.

This architecture allows the assistant to provide responses grounded in the platform's product knowledge base.

Context & System Configuration

The Settings module provides a central place for managing the platform's context layer, data sources, and system configuration.

This layer is important for AI applications because the quality of an AI response depends not only on the model itself, but also on the context and data available to the agent .

The architecture is designed to support integrations across databases, real-time context, AI services, and future automation workflows.